Hybrid Cars and HOV Lanes
Sharon Shewmake1
University of California, Davis
Lovell Jarvis
University of California, Davis
From 2005 to early 2007, California issued 85,000 Clean Air stickers
that allowed three models of hybrid cars to avoid congestion by driving
on High Occupancy Vehicle (HOV or ‘carpool’) lanes with a single
occupant. From data on used car prices, we estimate the willingness-topay for one of these Clean Air stickers as between $1,460 per sticker
per year, or $442 million for all 85,000 stickers. The high value of a
Clean Air sticker, and the comparatively small public benefits of the
program suggest that the program was not economically justified once
opportunity costs were taken into account. Instead the program
amounted to an inefficient giveaway to a limited number of hybrid car
enthusiasts. A program where HOV lane space is sold or auctioned to
raise funds for transportation improvements and air pollution mitigation
problems would increase efficiency and equity in comparison to the
provision of HOV rights to hybrid vehicles.
I. Introduction
Road traffic is projected to cost Californians over $42 billion per year in lost time and
higher fuel costs and is a significant contributor to California’s air pollution problems
(Klowden et al. 2009, CARB 2009). California has been on the cutting edge of creative
1
Financial Support was provided by the University of California Toxic Substances
Research & Teaching Program through the Atmospheric Aerosols & Health Lead
Campus Program. Special thanks to Jeff Goettsch for his assistance in gathering and
managing the online data, research assistance in gathering data was provided by Zachary
Prieston and Linh Dang. Nick Magnan, James Wilen, Doug Larson, as well as
participants at the 2009 EAERE FEEM VIU Summer School in Resources and
Environmental Economics, Camp Resources XVI, the 11th Occasional California
Workshop on Environmental and Resource Economics, the Heartland Environmental &
Resource Economics Workshop at Illinois and UC Davis Brown Bags all provided
helpful comments and discussion.
2
solutions to address congestion and air pollution externalities through the promotion of
low or zero emission vehicles, reformulated cleaner-burning fuels, and demand oriented
policies to reduce vehicle miles traveled (Gordon and Sperling 2009). This paper looks at
one of these programs. The California Clean Air program was aimed at encouraging the
adoption of energy efficient, low emission hybrid vehicles. The program offered special
stickers to hybrid car owners that allow them to allow bypass congestion by driving in
High Occupancy Vehicle2 (HOV) lanes without meeting minimum capacity constraints.
HOV lanes were built to induce drivers to carpool by providing a free flowing lane with
shorter travel times and greater travel time reliability. It was assumed that all drivers
would benefit from higher carpooling rates and fewer cars on the road. Society would
benefit from lower air pollution and lower fuel consumption. There is growing debate as
to whether or not an HOV lane is a strong enough incentive to carpool and even if it
were, whether or not more carpooling can really mitigate congestion (Kwon and Varaiya
2008, LAO 2000, Li et al. 2007, Dahlgren 1998). By 2004, it was clear that California’s
HOV lanes suffered from ‘empty lane syndrome’, when HOV lanes are under-utilized
and government officials feel pressure to convert them to general purpose lanes (Schofer
and Czepiel 2000). Moving a small fraction of cars from the general purpose lane to the
HOV lanes could relieve some congestion on the general purpose lanes without
worsening traffic on the HOV lanes. The question became how to allocate this space on
the HOV lanes.
One option was to convert HOV lanes into high occupancy/toll (HOT) lanes. In HOT
lanes, carpoolers can use the lane for free or a reduced toll and non-carpoolers pay the
full toll to use the lane. This policy was viewed as an entry into congestion pricing, and a
way to raise revenue for transportation projects. Alternatively clean air vehicles could be
allowed to drive on HOV lanes without meeting minimum-capacity requirement.
California chose the latter and instructed the Department of Motor Vehicles to issue
85,000 stickers to owners of qualifying hybrid vehicles. In this paper I show that the
85,000 stickers were worth approximately $442 million. Accordingly, the excess capacity
has a net present value of $1.77 billion.3 Converting 85,000 standard cars into hybrid cars
would be worth at most $197 million in air pollution benefits. Clean Air stickers were did
not even achieve that, previous studies have not found evidence that allowing hybrids
into HOV lanes encourages the adoption of hybrid cars.
A. The California Clean Air Sticker Program
In September of 2004, Governor Schwarzenegger signed Assembly Bill 2628 (AB 2628)
to allow hybrids meeting the state’s advanced technology partial zero emission vehicle
2
HOV lanes are also known as carpool lanes, express lanes, diamond lanes, commuter
lanes or transit lanes.
3
This estimate is calculated using a 7 percent discount rate.
3
(AT PZEV) standard and having a 45 mpg or greater fuel efficiency rating4 to use the
HOV lanes without having to carpool. Three hybrid vehicles met the requirements: the
Honda Civic hybrid, the Honda Insight and the Toyota Prius. 5
Fig. 1—Yellow Clean Air Stickers issued by the DMV to AT PZEV hybrid vehicles
From August 2005 to February 2007, any California driver with a Prius, Civic Hybrid or
Insight could write to the Department of Motor Vehicles (DMV) and obtain a set of
stickers for $8. If the owner sold his or her car, the sticker and the privileges it conferred
were transferred to the new owner of the vehicle. The 85,000 stickers were given out in
three installments. The first installment of 50,000 stickers was issued starting August
2005. Once those 50,000 stickers were issued, the DMV commissioned a study of the
impact of hybrids on HOV lanes. The study found hybrids had not degraded the HOV
lanes, so the remaining 25,000 stickers were issued under AB 2628. In September 2006,
another bill, AB 2600, expanded the number of stickers by 10,000 and extended the
program end date to January 1, 2011. Stickers were available for issue until February
2007, when the 85,000 limit was reached. After February 2007, a buyer could obtain a
California Clean Air sticker only by buying a used car that had stickers on it.6 Data from
the used car market provides observations on cars with and without stickers from which
one can estimate the value of a sticker. We hypothesize that this value is a valid indicator
of purchasers’ willingness to pay for access to California’s HOV lanes, conditional on
also driving a used qualifying hybrid.
TABLE 1
TIMING OF CALIFORNIA CLEAN AIR STICKER PROGRAM
September 2004
AB 2628 signed authorizing Hybrids to use HOV lanes in California
4
Additionally, 2004-model year or older hybrids with a 45 mile or greater fuel economy
rating and meeting either the SULEV, ULEV (ultra low emission vehicles) or PZEV
standards.
6
Effective January 1, 2009, DMV was allowed to issue Clean Air Stickers to the original
owners of qualifying hybrids to replace hybrids declared nonrepairable or total loss
salvage (AB 1209). Thus, it was possible for a 2008 or 2009 hybrid car to have a sticker
on it. As only one case was found in our data, it was not included.
4
August 2005
August 2005
September 2006
February 2007
September 2008
January 2009
January 2011
pending approval by federal government
SAFETEA-LU, federal transportation bill, authorizes states to allow fuelefficient hybrid cars into HOV lanes
State begins issuing California Clean Air stickers to qualifying hybrids
AB 2600 authorizes an additional 10,000 stickers and extends program life
till January 1, 2011
DMV completes distribution of California Clean Air stickers
AB 1209 allows hybrid owner with stickers whose cars are declared total
loss/salvage to obtain stickers for a new hybrid
DMV allowed to issue new stickers to owners of cars with stickers that
have been declared total loss/savage
California Clean Air sticker is scheduled to end.
B. Theoretical Value of a California Clean Air Sticker
The theoretical value drivers place on using HOV lanes depends on the level of
congestion on the general purpose lane versus the HOV lane, travel time savings from
using an HOV lane, the increased reliability in travel time from an HOV lane, drivers’
valuations of time, and whether or not the user feels safer in an HOV lane. In the Bay
Area, commuters who carpool report travel time savings of approximately 17 minutes
each way in 2005 (RIDES Associates 2005). Engineering estimates of travel time savings
vary depending on the network and time of day. Brownstone et al. (2007) find average
speed differences between HOV lanes and general-purpose lanes to be approximately 1030 percent across highways in Orange County, California. In the Bay Area, Kwon and
Varaiya (2008) conclude that HOV lanes provide only minimal travel time savings but do
provide better reliability.
Assuming that a sticker provides a service in each time period that is valued at , the net
present value of the sticker and the value of present and future services at time t is:
where T is the date at which the program will end and r is the rate of time preference.
Stickers may have had nominal value prior to February 2007.7 Thus we model the value
of a sticker prior to February 2007 as V1. Assuming the sticker service flow, ct, is
constant after February 2007 (i.e., ct=c for all t between February 2007 and January 1,
2011), the price of a sticker can be written as:
7
If stickers are available at the DMV for $8, they should not command a premium of
more than $8 plus some transaction cost in the overall vehicle price.
5
In the empirical section of this paper, we model the willingness-to-pay for a Clean Air
sticker as decreasing over time, both as a linear time trend and non-parametrically.
C. Hedonic Pricing Model
Hedonic pricing provides a method for decomposing a good into characteristics and
estimating the contributory value of each characteristic. Hedonic models have been
applied to a wide variety of goods. Court (1939) and Griliches (1971) are examples of
early hedonic models, both of which were applied to automobiles. Recent papers using
hedonic analysis to understand automotive markets include Kooreman and Haan (2006),
Ramachandran and Viswanathan (2005), Kahn (1986), and Berkovec (1985).
The value of a Clean Air sticker is first assumed to enter the automobile price linearly
and not as a function of other car characteristics. Thus, the price of a used car can be
written as:
The assumption of linearity is tested using a log transformation of price, common in the
hedonic literature. This specification implies the sticker’s value is multiplicative to the
value of the car:
Robustness checks on the price of the sticker are estimated by interacting the sticker price
with geographic regions, car characteristics, and gas prices. All of these relationships are
estimated in Appendix A.
II. Empirical Model
The price of used car i at time t is assumed to be a function of whether it has a Clean Air
sticker, car type (make, model, year, etc.), condition (captured by mileage), accessories ,
location of seller and whether financing is available. Price and the natural log of price are
both estimated, while car type is captured by model year and model type (Prius, Civic or
Insight). While many used cars were listed as being in “excellent” or “perfect” condition,
there was no objective way to grade the condition of the cars other than mileage and
whether the car had a salvage title.8
Theoretically, sticker value should be decreasing in time. We first allow the sticker price
to vary over time non-parametrically using a partially linear regression model. We then
fit the evolution of sticker price to a linear time trend to see how much the price of a
sticker changes on average.
The partially linear model allows the price of the sticker to be a non-parametric function
over time while controlling for other covariates with a simple regression model. We write
8
The impact of descriptive words such as “excellent”, “perfect” or “mint” on price was
examined for a subset of the data to no effect.
6
the price of the car as a function of the changing value of the sticker, car characteristics
and a normally distributed error term:
where is zero if the car does not have a sticker and takes on the value time t if the car
does have a sticker. F is a non-linear function tracing sticker price over time and refers
to other car specific characteristics such as make, model, mileage, etc. We use a variation
of Yatchew’s strategy (2003) to remove the non-parametric part of the regression so as to
consistently estimate , then form residuals
and run a non-parametric
kernel regression on the residuals:
.
Yatchew assumes that F is smooth with zi dense in the domain. All other variables are
assumed to be scalars, with the normal assumptions
and
.
The z’s are assumed to have bounded support and we can rearrange the data such that
. Yatchew assumes that the conditional mean of x is a smooth function of z,
E(x|z)=g(z), where g’ is bounded and Var(x|z)=σu2. Thus we can rewrite z=g(z) + u and
difference to obtain:
Since we assume that small changes in zi produce small changes in g and F. Thus the
direct effect of the nonparametric variable is removed and we can estimate β on the
transformed model using OLS.
The data from the used car market satisfies most of these assumptions in all but one case
where F is not smooth. The sticker is represented as a 0 if there is no sticker, and a 1
through 745 depending on the day during the two year period of May 19, 2008 and June
2, 2010 in which the car appears. While it is reasonable to assume that changes in sticker
value from week i to week i+1 are small, the change in automobile price from not having
a sticker to having a sticker on May 19, 2008 (week 1) is not small. Using Yatchew’s
strategy we can identify the weekly change in sticker value, but not the initial value of
having a sticker.
Instead we remove the non-parametric component of the model with a dummy variable
for each value that zi can take on in the data. This means we estimate almost 87 dummy
variables (the data spans 106 weeks, but not all weeks have data on cars with stickers)
and obtain imprecise estimates of sticker value. While the estimates of sticker value are
imprecise, we are able to estimate β using OLS without the bias from correlation between
xi and zi. We can then use to form residuals
, and run a non-parametric kernel
regression on:
=
.
We now turn to a description of the data and the estimation of the non-parametric and
linear models.
7
III. Estimated Value of a Clean Air Sticker
A. Description of the Data
Data were gathered from completed Ebay auctions and list prices from Autotrader.com.
Additional data was gathered from the classified sections of four major metropolitan
newspapers but there were not enough observations from newspapers to precisely
estimate a model. The breakdown of these data is presented in Table 2. Data were
gathered from Autotrader.com and Ebay manually for the month of May and July 2008,
and using a program from October 2008 to June 2010.
TABLE 2
SOURCES OF DATA AND DATE
Data Source
Ebay
Number of
Observations
231
Percentage of
Data
3.17
Autotrader.com
7,067
98.83
Total Complete Observations
7,298
Dates Covered in Data Segment
May 2008, July 2008, Oct 2008
through June 2010
May 2008, July 2008, Oct 2008
through June 2010
May 2008 – June 2010
Cars with a Clean Air sticker accounted for 14.0 percent of the cars in the sample. A
striking difference between cars with and without a Clean Air sticker is the difference in
mileage, presented in Table 3. Cars with a sticker are actually worth less on average, but
this is before taking mileage into account. The mileage of cars with a sticker is almost 50
percent higher than cars without. Cars with a sticker tended to be older than cars without
a sticker, and they had more miles per year driven. This could be because being able to
drive on HOV lanes makes driving less costly and more enjoyable. More likely, people
who expect to heavily use their cars applied for a sticker. Either explanation points to the
need to include mileage in any estimate of sticker value since it is correlated with the
Clean Air Sticker and an important component of price.
TABLE 3
AVERAGE PRICE AND MILEAGE FOR CARS WITH AND
WITHOUT CLEAN AIR STICKERS
Price
Cars with a Clean Air Sticker
Cars without a Clean Air Sticker
All Cars
$15,300
$16,800
$16,600
Average Age
of Car
4.2
3.2
3.4
Average
Mileage
72,500
50,300
53,400
Average
Mileage/Age
18,200
16,200
16,500
B. Results
The results for the regression of car characteristics with robustness checks are
summarized in Appendix A. Figure 3 shows the evolution of the sticker value over time
from the non-parametric regression with bootstrapped 95% confidence intervals. The
8
price of the sticker appears to be approximately $4,000 in May 2008, but falls to
approximately $1,000 by June 2010.
While Figure 2 provides compelling evidence that the sticker value is significant and
decreasing over time, looking at the entire sample we can see that there is more noise in
the beginning of the sample, this is because the program to automatically collect the data
was not running until late October 2009.
Fig. 2—Non-Parametric Estimation of Sticker Value Over Time
Another way to view the price of the sticker over time is to run a regression on a simple
linear model. In this section examine a model with an intercept for sticker price, α, and a
slope variable with coefficient γ:
I run this regression with the same set of controls and present the results in Table 4.
TABLE 4
LINEAR ESTIMATE OF STICKER PRICE OVER TIME
Variable
Sticker Intercept
Estimate
3,130***
(267)
Sticker Slope
-3.96***
(0.528)
Number of Observations
7,292
R-Squared
0.7589
NOTE: Robust Standard Errors are in parentheses, Asterisks denote significant
9
at the *10%, **5% and ***1% level.
The slope and intercept for the impact of sticker on vehicle price are significant and as
expected. The intercept indicates that a sticker was worth approximately $3,130 on May
19, 2008, and has since depreciated at approximately $4/day. This translates into yearly
values of $1,460.
Using a 7% discount rate and the lower weekly value, this means the stickers were worth
$5,200 each in January 2007, or $442 million for, all 85,000. If the state were to sell
yearly stickers, assuming symmetric demand, they could obtain $124 million per year or
a net present value of $1.77 billion. While these values seem high, in the next section we
will see that they fit in with previous value of time estimates and are likely a lower bound
of the value of access to HOV lanes.
C. Value of Time Estimates
The value of driving in the HOV lane has many components such as, travel time savings,
greater travel time reliability and a greater perception of safety by being able to travel in a
less congested lane. None of these effects can be separated using the data collected, but
as an empirical check a rough value of travel time savings can be estimated and
compared with previous results.
Assuming the Bay Area time savings are 17 minutes each way (RIDES Associates 2004),
similar to the 17 minutes of time savings found by Caltrans along the HOV corridor on I210 (Caltrans District 7 2006) and assuming commuters make two trips a day, five days a
week, the Clean Air sticker values time at $10 per hour. This is below the range of $2040 per hour found by Brownstone and Small (2005), but within the $7-25 per hour found
by Barrett (2010), $30 median value of time in Steinmetz and Brownstone and Steinmetz
(2005) and 50 percent of the gross wage rate found in Small (1992). The estimate of $10
per hour is likely an underestimate of HOV driving privileges since it is conditional on
having to drive one of three used hybrid cars and would likely be higher without that
constraint.
IV. Discussion
Excess capacity on HOV lanes did not have an obvious dollar value before the California
Clean Air program allowed motorists to capture significant economic rents. Allowing
stickers to be traded enables those rents to be observed. If the excess capacity
calculations were correct (Boriboonsomsin and Barth 2008, Brownstone et al. 2007,
Breiland and Benouar 2006), and hybrids did not slow down carpoolers, then the state
created up to $612 million9 of economic rent through the California Clean Air program.
9
Using the higher estimate of $2,040/sticker/year.
10
The Clean Air Program is set to expire January 1, 2011, and the California State
Assembly is debating what to do with the excess capacity once the current Clean Air
stickers expire. Senate Bill 535, which is sponsored by General Motors, would allow the
next generation of hybrids, the plug-in electric hybrids such as the Chevy Volt, to use
HOV lanes. Our results suggest that a repeat of the Clean Air Program would not be the
best use of the excess capacity. This section discusses whether or not the original
California Clean Air program achieved its goal of stimulating the market for hybrid cars,
reducing air pollution and alleviating congestion. I also discuss implications of forgone
revenue, and the potential for alternative uses of excess capacity, particularly HOT lanes.
California Clean Air stickers were one of many incentives created to encourage the
purchase of hybrid cars at the state, federal and local levels. A natural question to ask is
whether granting access to hybrids stimulated the demand for hybrids. Three studies have
addressed this question. Gallagher and Muehlegger (2008) and Chandra et al (2008)
found that HOV privileges were not strongly correlated with hybrid market share in the
US or in Canada. Diamond (2009) found HOV privileges encouraged hybrid ownership
in northern Virginia, but he did not find an impact in other states or in other parts of
Virginia with HOV lanes.
My research indicates that HOV privileges have substantial value to motorists, and
should stimulate demand for hybrid cars. One reason they may not have stimulated
demand in California is that that stickers were given away to vehicles that had already
been purchased.
Figure 3 displays the number of cars with a sticker in my sample by model year. The
stickers were given out starting in August 2005, by then 2006 model year cars were being
sold as new models. According to the data I gathered on Autotrader, Ebay and from
classified ads, 32% of cars were 2004 or earlier model years while 62% were 2005 or
earlier model years. California Clean Air Stickers were not available until the 2006
models were being sold. Almost two-thirds of the stickers were given to cars that were
already on the road. It is of dubious value to give a car purchased in 2000 a sticker to
encourage its purchase in 2006.
11
Fig. 3—Number of Cars with HOV Stickers by Model Year
Gallagher and Muehlegger (2008), Chandra et al (2008) and Diamond (2009) look at
cross-state comparisons to explore the impact of HOV privileges on hybrid purchases.
Hybrids were already popular by the time AB 2628 was passed to encourage their
purchase. AB 2628 was introduced in early 2004, signed into law in September 2004, and
initiated in August 2005. Figure 4 shows when AB 2628 was introduced into the State
Senate as well as when stickers were actually available. These data are overlaid with
Department of Motor Vehicle data for the total number of hybrid cars registered in
12
California by model year.
Fig. 4—Hybrids Registered in California
Bars indicate when AB 2628 was introduced and when the Clean Air Stickers were actually available.
Source: California Department of Motor Vehicles
The data in Figure 4 is also presented in Table 5. Using DMV data made available by
Jeffrey Williams, we looked at the total number of cars by model year in California that
were ever registered in California. This section remains incomplete. The data from the
DMV is new, and has missing observations for Priuses in year 2001, 2002 and 2003.
TABLE 5
CAR REGISTRATIONS IN CALIFORNIA BY MODEL YEAR
Number of Cars By Model
Model Year
Prius
Civic
Insight
2000
0
0
1,246
2001
0
0
1,099
2002
0
0
565
All Models Cumulative
1,246
1,246
1,099
2,345
565
2,910
13
2003
2004
2005
2006
2007
2008
0
15,145
36,353
24,640
44,536
42,254
8,976
6,323
7,703
8,486
9,235
7,283
284
114
128
117
0
0
9260
21,582
44,184
33,243
53,771
49,537
12,170
33,752
77,936
111,179
164,950
214,487
The name California Clean Air Sticker signifies that the program was intended to lessen
pollution from automobiles. If we make the most conservative assumptions possible,
namely ignoring evidence that the Clean Air stickers failed to stimulate hybrid sales and
thus assuming that each of the 85,000 stickers caused a conventional car to be replaced
with a hybrid, and making conservative assumptions about the value of air pollution, the
upper limit to the value of air pollution reductions is $197 million (for calculations see
Appendix C). If all 85,000 permits had been sold for $3,400 each, the state could have
obtained $306 million in revenue to reduce air pollution from more cost effective sources.
Another aim of the program was to reduce congestion. An optimal taxation scheme
would favor workers over non-workers during the peak period (De Borger 2009). To the
extent that commuters obtained stickers, the Clean Air Sticker program might have
lessened the deadweight loss of congestion by allowing those with a high value of time to
“buy out” of congestion by purchasing hybrid cars. As Parry (2002) finds, the biggest
efficiency gains from congestion pricing come from separating high value and low value
users into fast and slow lanes, not necessarily from encouraging low value users to use
public transit or travel during off-peak times. While this market for Clean Air stickers
may have been unanticipated by policy makers, it likely improved welfare by allowing
drivers with a high value of time to bypass congestion through purchase of a hybrid car
with a sticker. This does not mean the entire California Clean Air policy was justified on
welfare grounds, just that the market for stickers likely improved welfare in comparison
to programs where stickers could not be transferred between owners.
Assuming that the excess capacity on the HOV lanes really can be used without
impacting carpoolers, using the excess capacity is worth at least $85-170 million/year.
The California Clean Air sticker program is not the best way to use that capacity. It is a
marginal improvement on a system that is already far from optimal and better marginal
improvements can be made. Auctioning stickers for hybrids would have raised
government revenue for a direct hybrid subsidy with money leftover. Auctioning stickers
for any type of car would have raised even more government revenue and would have
allocated stickers to those with the highest value of time. If the government still wanted
to subsidize hybrids, they could have issued a revenue equivalent subsidy which has been
shown to be more effective. Instead of handing out access privileges as a lump sum,
transportation officials could have created HOT lanes that allow anybody willing to pay
to bypass congestion. This would have been more equitable since it would have allowed
all motorists, regardless of vehicle choice, to escape traffic regularly or occasionally
instead of just a lucky 85,000.
14
V. Conclusion
An un-priced highway will suffer from overuse if travelers do not take into account the
external costs of pollution, congestion, accidents and road maintenance. An optimal
pigouvian tax would result in consumers choosing the socially efficient combination of
energy efficient cars, number of trips and mode choice, but political concerns have
blocked congestion and emission charges. This leaves policy makers with technical fixes
for pollution and ad hoc methods to discourage driving such as driving restrictions (Lucas
2009), subsidies for transit, or occupancy restrictions (HOV lanes). It is unclear whether
HOV lanes result in more carpools, higher social surplus or less pollution, and they are
not necessarily more effective than a general purpose lane (Dahlgren 1998). Giving away
excess capacity on HOV lanes to hybrid cars is an ad hoc measure on top of an already
wasteful policy.
California considered using the excess capacity of HOV lanes to experiment with a form
of congestion pricing, but instead chose to grant access to its HOV lanes to owners of
hyrid cars. These cars achieve higher gas mileage, reducing smog forming pollutants and
greenhouse gases. Always forward looking, California was hoping that if motorists could
be convinced to switch from conventional cars to hybrids, it could speed a transition to
even more advanced technologies such as plug-in hybrids, natural gas vehicles or electric
vehicles. This research suggests that granting stickers to hybrids did not achieve either of
these goals. The air pollution benefits are worth far less than the value of the stickers, and
research by others shows that stickers are less effective than direct subsidies. From a
welfare perspective, allowing users with the highest values of time to bypass traffic can
contribute to the overall efficiency of the system and so auctioning permits would have
been more effective in terms of efficiency and raising revenue for a cash-strapped state.
The California Clean Air sticker program failed to achieve its goals and came at a high
opportunity cost to the state. Similar programs in Arizona, Colorado, Florida, Georgia,
Hawaii, Maryland, Texas, Utah and Virginia allow clean air vehicles access to HOV
lanes. The value of access to HOV lanes varies within California and is likely to vary
across states, but traffic managers in all these areas need to carefully analyze programs
that give out access without regard as to whether this is the best way to use this capacity.
Arizona considered selling its capitol building to raise funds, California is facing massive
shortfalls. Instead of using the valuable space in HOV lanes to fund education, health,
transportation or a variety of public work projects, these states are giving away space to
support an ineffective project.
References
Bajari, P. and C.L. Benkard. (2005) Demand Estimation with Heterogenous Consumers
and Unobserved Product Characteristics: A Hedonic Approach. Journal of
Political Economy, Vol 113, No. 6, Pgs. 1239-1276.
Barrett, J. (2010) Unpublished PhD Dissertation, Department of Agricultural and
Resource Economics, University of California, Davis
Bay Area Toll Authority. (2009) Tolls and Traffic,
15
http://bata.mtc.ca.gov/tolls/schedule.htm, <Accessed January 18, 2009>.
Boriboonsomsin, K. and M. Barth. (2008) Impacts of freeway high-occupancy vehicle
lane configuration on vehicle emissions. Transportation Research Part D, Vol.
13, Pgs. 112-125.
Breiland, C., L. Chu and H. Benouar. (2006) Operational effect of allowing single
occupant hybrid vehicles into high occupancy vehicle lanes. 2006 TRB Annual
Meeting, paper #06-2813.
Brownstone, D., L. Chu, T. Golob, K. Nesamani and W. Recker. (2007) Evaluation of
incorporating hybrid vehicle use of HOV lanes, Draft Final Report for PATH to
5315.
Brownstone, D. and K. Small. (2005) Valuing time and reliability: assessing the evidence
from road pricing demonstrations, Transportation Research Part A, Vol. 39, Pgs.
279-293.
Brownstone, D., A. Ghosh, T. Golob, C. Kazimi, D. Amelsfort. (2003) Drivers’
willingness-to-pay to reduce travel time: evidence from the San Diego 1-15
congestion pricing project, Transportation Research Part A, Vol 37, Pgs. 373387.
California Air Resources Board (CARB) (2009) Emissions Inventory Data,
<http://www.arb.ca.gov/ei/emissiondata.htm>, Accesssed January 26, 2010.
California Department of Transportation (Caltrans), District 7. (2006) 2006 HOV Annual
Report. http://www.dot.ca.gov/dist07/
aboutdist7/pubs/hov_annual/2006/06%20HOV%20Annual%20Report%20rev%2
010_19_06.pdf, Accessed July 23, 2009.
Chandra, A., S. Gulati and M. Kandlikar (2009) Green Drivers or Free Riders? An
Analysis of Tax Rebates for Hybrid Vehicles, Working Paper,
http://ssrn.com/abstract=1348808
De Borger, D. (2009) Commuting, congestion tolls and the structure of the labour market:
Optimal congestion pricing in a wage bargaining model, Regional Science and
Urban Economics, Vol. 39, Issue 4.
Downing, J. (2009) Battle brews in California over expanding carpool-lane access, The
Sacramento Bee, October 16.
Gallagher, K. and E. Muehlegger. (2008) Giving Green to Get Green: Incentives and
Consumer Adoption of Hybrid Vehicle Technology, Faculty Research Working
Papers Series, Harvard University.
Golden Gate Bridge. (2009) Tolls and Traffic Operations,
http://goldengatebridge.org/tolls_traffic/toll_rates_carpools.php, <Accessed
January 18, 2009>.
Griliches, Z. (1971) Hedonic price indexes of automobiles: an econometric analysis of
quality change, in Griliches, Z. (ed.), Price Indexes and Quality Change,
Harvard University Press, Cambridge.
Halvorsen, R. and R. Palmquist. (1980) The Interpretation of Dummy Variables in
Semilogarithmic Equations, The American Economic Review, Vol. 70, No. 3, Pgs
474-475.
Kahn, M. (2009) California’s Green Market Geography, Southern California
Environmental Report Card, UCLA Institute of the Environment,
http://www.ioe.ucla.edu/reportcard/article.asp?parentid=2304, <Accessed October
13, 2009>
16
Kahn, J. (1986) Gasoline Prices and the Used Automobile Market: A Rational
Expectations Asset Price Approach, Quarterly Journal of Economics, Vol. 101,
No. 2, Pgs. 323-340.
Kooreman, P. and M. Haan. (2006) Price Anomalies in the Used Car Market, De
Economist Vol. 154, Pgs. 41-62.
Kwon, J. and P. Varaiya. (2008) Effectiveness of California’s High Occupancy Vehicle
(HOV) system, Transportation Research Part C, Vol. 16, Pgs. 98-115.
Legislative Analyst’s Office, (2000) HOV Lanes in California: Are they achieving their
goals?, http://www.lao.ca.gov/2000/010700_hov/010700_hov_lanes.html,
<Accessed December 15, 2008>.
Li, J., P. Embry, S. Mattingly, K. Sababadi, I. Rasmidatta and M. Burris, (2007) Who
Chooses to Carpool and Why? Examination of Texas Carpoolers, Transportation
Research Record: Journal of the Transportation Research Board, No. 2021, 110–
117.
Morton, F., F. Zettelmeyer and J. Silva-Risso. (2001) Internet Car Retailing, The Journal
of Industrial Economics, Vol. 49, No. 4, Pgs. 501-519.
National Public Radio. (2007) Access to Calif. HOV lane puts premium on hybrids. April
17, http://www.npr.org/templates/story/story.php?storyId=9626745, <Accessed
January 20, 2009>.
Palmquist, R. (2003) Property value models, in Maler and Vincent (eds) Handbook of
Environmental Economics, Vol. 2.
Parry, I.W.H. (2002) Comparing the efficiency of alternative policies for reducing traffic
congestion, Journal of Public Economics, Vol. 85, Pgs. 333-362.
Pavley, F. (2004) Assembly Bill No. 2628, California State Senate,
<http://www.leginfo.ca.gov/pub/03-04/bill/asm/ab_26012650/ab_2628_bill_20040923_chaptered.pdf> Accessed May 5, 2009.
Pavley, F. (2004) Assembly Joint Resolution No. 74, California State Senate,
<http://www.leginfo.ca.gov/pub/03-04/bill/asm/ab_00510100/ajr_74_bill_20040708_chaptered.pdf> Accessed May 5, 2009.
RIDES Associates. (2005) Commute Profile 2005, Prepared for the Metropolitan
Transportation Commission’s Regional Rideshare Program.
Rosen, S. (1974) Hedonic prices and implicit markets: product differentiation in pure
competition, Journal of Political Economy, Vol. 82, Pgs. 34-55.
San Joaquin Valley Unified Air Pollution Control District (2008) Update to Rule 2201
Best Available Control Technology (BACT) Cost Effectiveness Thresholds, Final
Staff Report. May 18.
Schofer, Joseph L., and Edward J. Czepiel. Success Factors and Decision Issues for
High-Occupancy Vehicle Facilities. In Transportation Research Record, No. 1711,
TRB National Research Council, Washington D.C., 2000, pp. 13-22.
Small, K. and C. Kazimi. (1995) On the Costs of Air Pollution from Motor Vehicles,
Journal of Transport Economics and Policy, Vol. 29, No. 1, Pgs. 7-32.
Small, K. (1992) Urban Transportation Economics, Fundamentals of Pure and Applied
Economics Series, Vol. 51, Harwood Academic Publishers, New York.
Smith, V. and J. Huang. (1993) Hedonic models and air pollution: twenty-five years and
counting, Environmetnal and Resource Economics, Vol. 3, Pgs. 381-394.
Sperling, D. and D. Gordon. (2009) Two Billion Cars: Driving Toward Sustainability,
Oxford University Press, Oxford, UK.
17
Steinmetz, S. and D. Brownstone. (2005) Estimating commuters’ “value of time” with
noisy data: a multiple imputation approach, Transportation Research Part B, Vol.
39, Pgs. 865-889.
Yatchew, A. (2003) Semiparametric Regression for the Applied Econometrician,
Cambridge University Press. Cambridge, UK.
Appendix A: Regression Tables
The regressions from the body of the paper are presented here. Model 1 is the partially
linear model where weekly dummies are used to remove the non-parametric effect.
Models 2 is the linear model. Many of the coefficients of car characteristics are positive
and go in the direction economic theory would predict. The coefficients for mileage range
from -0.054 to -0.056. This translates into a penalty of $54-$56 for every thousand miles
on the vehicle. Ramachandran et al. (2006) find values of $31.08 and $36.65 per
thousand miles on the vehicle, without controlling for mileage squared, miles driven per
year or a dummy variable for crossing the 100,000-mile mark. Mileage squared is not
significant in any of the regressions, but miles per year is negative and significant across
regressions, as is the dummy variable for having mileage over 100,000 miles. Having a
salvage title is significant across all regressions and results in an approximately $4,000
penalty on the car price.
Drivers that expect to use their vehicle heavily will be more likely to apply for a sticker
and may want a car with more extras such as a premium sound or a navigation system. A
built-in navigation system is worth approximately $260, significantly less than the
dealership price differential of $2,000 for cars with and without a navigation system. A
premium (JBL) sound system is worth somewhere approximately $800, while mp3
playing capabilities are worth a little over $600. Leather interiors increase the price of a
car by about $700 across the regressions, while a ‘loaded’ package has no statistical
significance and Bluetooth capabilities is only significant at the 10% level in one of the
regressions.
Where the car was sold was an important determinant of price. Cars sold on Ebay (the
omitted category) commanded a $3,800 lower price than asking prices were on
Autotrader and were lower than asking prices in the four newspapers. Using asking prices
instead of actual prices should not be a problem in the identification of the sticker
because the difference between the asking price and the actual price should not vary
depending on whether or not the car has a sticker. Prices from dealers were about $800
more than prices from private sellers. Monthly dummies on the price of all cars and
dummy variables for make/model combinations are not presented, but they are as
expected. While not all of these results are relevant to the price of the sticker, they do
lend credibility to the model.
TABLE 6
REGRESSION COEFFICIENTS
Variables
Model 1
Partially Linear
Model 2
Linear Estimate
18
Mileage
-0.0846***
-0.0849***
(0.003)
Mileage Squared
Mileage over 100,000 Miles
Salvage Dummy
Premium Sound System
MP3 Player
Leather Interior
Listed at “loaded”
Bluetooth Capabilities
Navigation System
Ebay Listing (Autotrader
omitted)
San Diego Region
(LA Omitted)
San Francisco Region
Sacramento Region
Central Valley Region
Northern California Region
Sold by a Private Seller
Gas Price (California
Regular, Statewide in cents)
Make/Model Dummy
Variables
Date Dummies
Constant
1.74E-07***
(1.54E-08)
-473.4***
(131.8)
-4379***
(232.5)
862.6***
(104.8)
335.3***
(90.5)
598.0***
(207.0)
-696.0***
(121.7)
-167.2
(153.4)
253.5***
(83.4)
-3672***
(193.4)
-4694***
(98.9)
-43.8
(59.8)
-354.9***
(94.7)
247.1**
(117.4)
225.9
(336.3)
-1076.8***
(142.1)
20.31***
(3.76)
Yes
Yes
14014***
(0.003)
1.76E-07***
(1.51E-08)
-493.9***
(130.7)
-4388***
(229.4)
862.2***
(105.3)
334.6***
(90.6)
601.4***
(113.9)
-696.3***
(120.5)
-168.6
(153.5)
248.1***
(83.1)
-3628***
(195.0)
-480.9***
-54.3
(59.3)
-360.9***
(94.3)
246.4**
(116.4)
192.7
(332.8)
-1025.4***
(134.9)
19.0***
(3.10)
Yes
Yes
14721***
(1342)
(1109)
Observations
7292
7292
R-squared
0.762
0.759
NOTE: Robust standard errors are in parentheses, Asterisks denote significant at
the *10%, **5% and ***1% level.
19
Appendix B: Alternative Specifications
In this section we examine how a Clean Air Sticker changes with alternative
specifications. First we look at whether or not the sticker’s value changes depending on
using a multiplicative model versus an additive model. Next we explore how gas prices
interact with sticker and vehicle price. Finally we look at whether or not the sticker is
more valuable in various metropolitan areas and the impact on the sticker price of various
car characteristics such as the make, year, mileage, and options on a vehicle.
A. Natural Log Specification
The first specification we examine is transforming the price with natural logs. This
implies that the sticker and other car attributes enter the price multiplicatively, where
Priceit refers to the price of the automobile, xi is a vector of vehicle characteristics and F
is a non-parametric function meeting the assumptions described in Section II:
Fig. 5—Non-Parametric Estimation of Sticker Value Over Time Using Ln(Price)
Taking the natural log of both sides, this results in the transformed model:
Using the same technique as in the body of the paper, we generate a graph that trace the
price of the sticker over time. This is presented in Figure 5. Figure 5 has a similar shape
when compared with Figure 2, which was made using a regression on simply the price, as
20
opposed to the natural log of price, of a vehicle. The main differences is that the log
model appears to have a negative premium in late May/early June.
In addition to the partially linear model, we examine a model with an intercept for sticker
price, α, and a slope variable with coefficient γ:
.
We present the results in Table 7.
TABLE 5
LINEAR ESTIMATE OF STICKER PRICE OVER TIME
USING LN(PRICE)
Variable
Sticker Intercept
Estimate
0.155***
(0.15)
Sticker Slope
-0.0002315***
(0.0000302)
Number of Observations
7,292
R-Squared
0.8070
NOTE: Robust Standard Errors are in parentheses, Asterisks denote significant
at the *10%, **5% and ***1% level.
Again, many of the results in Table 7 are similar to the analogous untransformed model
results in Table 5. The magnitude of the intercept and the slope show the same patterns
across the three models in Tables 5 and 7. Transforming the natural log model using the
Halvorsen-Palmquist adjustment (1980) we find that the average daily value of a sticker
is $3.85. This is eleven cents per day less than the additive model. The additive model is
simpler and more intuitive than the multiplicative model. In discussions of the impact of
a sticker value, on PriusChat, in Autotrader ads and in the popular press, a sticker was
discussed as if it added something to the price, not multiplied the price by something.
B: Regional Interactions with Sticker Price
Each observation from AutoTrader or Ebay came with a dealer address or a zip code in
the case of a private seller. Since vehicles are an expensive but mobile expense, it is safe
to assume some level of market integration across metropolitan regions, and so we
grouped the observations into six regions: Los Angeles, San Diego, San Francisco,
Sacramento, Northern California (north of the Bay Area and Sacramento) and the Central
Valley. To test how location impacts the price of the sticker, we include an interaction
term between sticker and the six regions in California. The dummy variables for each
region are all negative, which shifts the non-parametric estimates of sticker value over
time. Figure 6 shows a function that has roughly the same shape as previous nonparametric regressions but a slightly higher anchor point. The likely reason is that the
anchor point is not precisely estimated in the data. The coefficients in Table 6 are all
negative, even if only one is statistically significant.
21
Fig. 6—Sticker Values Over Time Controlling For Regional Sticker Interactions
Fig. 7—Sticker Values Over Time Controlling For Regional Sticker Interactions Using
Ln(Price)
TABLE 6
IMPACT OF REGION ON STICKER PRICE
22
Region (Los Angeles
Omitted)
San Diego
San Francisco
Sacramento
Central Valley
Northern California
Price
Ln(Price)
-330.4
(318.5)
-328.0
(206.5)
-317.4
(457.7)
-1034
(752.1)
-669
(741.4)
-0.0154
(0.0189)
-0.0231*
(0.0122)
-0.04207
(0.0334)
-0.0480
(0.0438)
-0.0245
(0.0547)
NOTE: Robust Standard Errors are in parentheses, Asterisks denote significant
at the *10%, **5% and ***1% level.
Modeling the evolution of the sticker over time as a linear function, we again look at
whether or not the sticker’s value depends on the metropolitan area. We find similar
results in Table 7. Additionally we look at the type of car being sold, the year and
mileage of the car, and the types of options packages available with the sticker. We also
run a model with both region and vehicle characteristics interacted with the presence of
the sticker. These models are presented in the last two columns of Table 10. In the last
two regressions, Stata drops the HOV Intercept, which leaves all the region-HOV
dummies as significant. This is not because adding the other car characteristics makes
regional interactions suddenly significant but is an artifact of Stata’s decision to drop the
HOV intercept. None of the regions are statistically different from one another.
Vehicle characteristics that did influence the value of the sticker were the model of the
car, the mileage on the car, whether or not the car had leather seats and a premium JBL
sound system. The sticker added the most value to an Insight, followed by the Prius. One
significant difference between the natural log model and the additive model was the
impact of mileage on sticker price. In the natural log model a sticker was worth less on a
vehicle with high mileage than a vehicle with low mileage, but in the additive model
there was no statistical difference. Interestingly a sticker on a vehicle with an upgraded
sound system was worth less than a sticker on a vehicle without an upgraded sound
system, but bluetooth and mp3 capabilities, as well as a navigation system or being listed
as ‘loaded’ had no effect on the value of a sticker.
TABLE 7
INTERACTIONS BETWEEN STICKER VALUE AND VEHICLE CHARACTERISTICS
Variable
Intercept
HOV Slope
Los Angeles
Region
San Diego
Region
S.F. Region
Price
4,414***
(697)
-28***
(6.56)
-144
(556)
-1177
(837)
-566.6
(571)
Ln(Price)
0.233***
(0.419)
-0.0013***
(0.00037)
-0.00146
(0.404)
-0.0612
(0.0543)
-0.0227
(0.0411)
Price
6,293***
(1,125)
-20.1***
(6.12)
Ln(Price)
0.385***
(0.105)
-0.0011***
(0.00344)
Price
dropped
Ln(Price)
dropped
-19.7***
(6.64)
6,954***
(1,270)
5,856***
(1,280)
6,450***
(1,211)
-0.0011***
(0.00038)
0.427***
(0.113)
0.362***
(0.121)
0.399***
(0.111)
23
Sacramento
Region
Central Valley
dropped
7,245***
0.433***
(1,289)
(0.112)
-375.4
-0.00379
7,039***
0.435***
(768)
(0.0471)
(1,373)
(0.119)
Prius (Insight
-1,640*
-0.160*
-2,120**
-0.188*
Omitted)
(840.2)
(0.0961)
(921)
(0.101)
Civic
-2,572***
-0.202**
-3,070
-0.231**
(841.0)
(0.0950)
(925)
(0.100)
Mileage
-0.014***
-2.77e-07
-0.014***
-2.99e-07
(0.00477)
(4.29e-07)
(0.00473)
(4.29e-7)
Leather
788*
0.0418*
778*
0.0407*
(455)
(0.0240)
(463)
(0.0243)
Loaded
503
0.0286
491
0.0270
(399)
(0.0233)
(399)
(0.0230)
JBL
-867**
-0.050**
-859**
-0.0492*
(429)
(0.240)
(433.6)
(0.024)
Bluetooth
-451
-0.0058
-430
-0.00451
(520)
(0.0290)
(522)
(0.0290)
MP3
-196
-0.00079
-172
0.00037
(485)
(0.268)
(479)
(0.0264)
Navigation
-236
-0.00768
-246
-0.00786
(360)
(0.0208)
(359)
(0.0204)
R-Squared
0.7197
0.7689
0.7221
0.7700
0.7228
0.7706
NOTE: Robust Standard Errors are in parentheses, Asterisks denote significant at the *10%, **5% and ***1%
level.
C. Gas Prices and Vehicle Characteristics Interactions with Sticker Price
Gas prices may influence the price of a sticker so regressions were included to examine
the impact of gas prices on both the value of a hybrid vehicle as well as the value of the
Clean Air sticker. Statewide weekly retail prices for a gallon of California regular
gasoline were included both directly on the price of a car, as well as interacted with the
Clean Air Sticker. These results are presented in Table 8.
TABLE 8
THE IMPACT OF THE STATEWIDE PRICE OF CALIFORNIA REGULAR
GASOLINE ON VEHICLE AND STICKER VALUE
Variable
Gas Price (cents)
Price
Ln(Price)
25.02***
0.000571***
(3.03)
(0.000180)
Gas Price (cents) * Sticker
7.12***
0.000086
(2.61)
(0.00013)
HOV Intercept
1,156
0.1818***
(1,167)
(0.0677)
HOV Slope
-15.6**
-0.00119***
(6.30)
(0.000391)
R-Squared
0.7201
0.7685
NOTE: Robust Standard Errors are in parentheses, Asterisks denote significant at the *10%, **5% and
***1% level.
24
This regression shows the influence of gas prices (in cents) on the value of the California
Clean Air sticker and the price of a hybrid car. The price of gas is from the Energy
Information Agency. There exists considerable variation in the data, between $1.73 and
$4.59. The price of gas has a significant impact on both the price of a vehicle and a
smaller impact on the value of a sticker as can be seen in Table 8. For every one cent
increase in the price of gasoline, a hybrid vehicle is worth approximately $0.25 more,
while a sticker increases in price by $0.07.
Appendix C: Calculation of Air Pollution Benefits from Clean Air Stickers Program
In this section I analyze the two criteria pollutants that California is not yet in attainment
for and which light duty passenger vehicles are a major contributor, NOx and volatile
organic compounds (VOC) as well as green house gases as measured by carbon dioxide
equivalence, CO2e. Hybrid cars produce 90 percent less NOx than the average car. The
standard for 2000 model year passenger automobiles was 0.4 grams of NOx per mile10. If
every driver who purchased a hybrid vehicle with a Clean Air sticker would not have
purchased a sticker otherwise and those drivers would have driven the same amount11
with their conventional car, 3,204 tons of NOx were reduced from 2006 to 2011. If we
assume the vehicles lasted 10 years on average, this resulted in 6,408 tons of NOx.
NOx = 0.90 x 0.4 grams/mile x 19,000 miles/year x 10 years x 85,000 cars
= 6,408 tons of NOx
Using the 2000 standard of 0.090 grams/mile of VOCs, and assuming that hybrids emit
0.010 grams/mile of VOCs, then using the same assumptions below we have that this
program prevented 1,420 tons of VOC from being emitted.
VOC = (0.090 - 0.010) grams/mile x 19,000 miles/year x 10 years x 85,000 cars
= 1,420 tons of VOCs
If each one of the cars with 45 miles per gallon (0.022 gallons/mile) had been replaced
with a conventional car meeting CAFE requirements of 27.5 miles per gallon (0.036
gallons/mile) and each car lasted 10 years, then 604,000 tons of CO2e was reduced by
this program.
CO2 e= 8.788 kilograms CO2/gallon12 x 0.014 difference in gallons gasoline/mile
x 19,000 miles/year x 10 years x 85,000 cars x 100/9513
10
0.4 grams per mile of NOx was the standard for 2000 model year cars. The median
model year for a car with a Clean Air sticker is 2005 which falls under a stricter standard.
11
The average passenger car is driven approximately 12,000 miles per year, hybrid cars
with a Clean Air Sticker in the sample are driven 19,000 miles per year. In general,
increases in energy efficiency lead to more intensive uses, in this case a more efficient car
will likely be driven more due to its lower per mile cost. This is known as the ‘rebound
effect’.
12
http://www.epa.gov/OMS/climate/420f05004.htm
25
= 1.9 million tons of CO2e
Another assumption is that every person buying a hybrid that obtained a Clean Air sticker
did so because of the sticker. This assumption is clearly overly conservative as many
people are buying hybrid cars even without the incentive and the research indicates the
Clean Air sticker program did not increase the demand for hybrid cars. Even under these
conservative assumptions, at $50/ton of CO2e, $15,00/ton of NOx, and $4,100/ton of
VOC14, the state could have reduced the same amount of air pollution for $197 million.
13
The EPA estimates converts CO2 emissions into CO2e from automobiles by assuming
that N2O, CH4 and HFCs make up approximately 5% of GHG emissions from
automobiles after accounting for the greenhouse warming potential of each gas. Hence
recommends multiplying by 100/95 to convert CO2 to CO2e is recommended.
14
Valuation of NOx and VOC from Small and Kazimi (1995)’s review of the costs of air
pollution in California from motor vehicles, adjusted for 2009 dollars.